Overall results: Table REF  reports the mAP for all approaches on four tasks: ZSD, ZSMD, ZST, and ZSMT across different combinations of network architectures. We can make following observations: (1) Our cluster based method outperforms other competitors on all four tasks because its loss utilizes high-level semantic relationships from meta-class definitions which are not present in other methods.
(2) Performances get improved from baseline to Ours (with \(L^{\prime }_{mm}\) ) across all zero-shot tasks. The reason is baseline method did not consider word vectors during the training. Thus, overall detection could not get enough supervision about the semantic embeddings of classes. In contrast, \(L^{\prime }_{mm}\)  loss formulation considers word vectors.
(3) Performances get improved from ZST to ZSMT across all methods whereas similar improvement is not common from ZSD to ZSMD. It's not surprising because ZSMD can get some benefit if meta-class of the predicted class is same as the meta-class of true class. If this is violated frequently, we cannot expect significant performance improvement in ZSMD.
(4) In comparison of traditional object detection results, ZSD achieved significantly lower performance. Remarkably, even the state-of-the-art zero-shot classification approaches perform quite low e.g., a recent ZSL method [1]} reported \(11\%\)  hit@1 rate on ILSVRC 2010/12. This trend does not undermine to significance of ZSD, rather highlights the underlying challenges.
